{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Santander Keggle Competition -- predict dissatisfacted customers \n",
    "Which customers are happy customers? From frontline support teams to C-suites, customer satisfaction is a key measure of success. Unhappy customers don't stick around. What's more, unhappy customers rarely voice their dissatisfaction before leaving. Santander Bank is asking Kagglers to help them identify dissatisfied customers early in their relationship. Doing so would allow Santander to take proactive steps to improve a customer's happiness before it's too late.   \n",
    "https://www.kaggle.com/c/santander-customer-satisfaction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load library\n",
    "load libraries to be used in this project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load libraries\n",
    "import pandas\n",
    "import numpy as np \n",
    "from sklearn import cross_validation\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import ensemble\n",
    "from sklearn import cross_validation as cv\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "from sklearn import linear_model \n",
    "from sklearn import naive_bayes \n",
    "from sklearn.svm import SVC\n",
    "from sklearn import preprocessing\n",
    "import time\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Tell iPython to include plots inline in the notebook\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 1. Pre-process\n",
    "The code below will load the training and testing dat; conduct a data aduting; remove constant and dupicated colums"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "df_train = pandas.read_csv(\"train.csv\")\n",
    "df_test  = pandas.read_csv(\"test.csv\")   \n",
    "print(\"Data loaded\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Data Summary\n",
    "conduct a briedf data auditing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set:\n",
      "Total number of data: 76020\n",
      "Number of features: 369\n",
      "Number of satisfied customer: 73012\n",
      "Number of dissatisfied customer: 3008\n",
      "% of dissatisfied customer: 4.00%\n",
      "\n",
      "Testing set:\n",
      "Total number of data: 75818\n",
      "Number of features: 369\n"
     ]
    }
   ],
   "source": [
    "# data summary \n",
    "# training set\n",
    "print (\"Training set:\")\n",
    "n_data  = len(df_train)\n",
    "# exclude first colum ID and last column label (substract 2)\n",
    "n_features = df_train.shape[1]-2 \n",
    "# number of satisfied customers\n",
    "n_sat = len(df_train[df_train['TARGET'] == 0]) \n",
    "# number of dissatisfied customers\n",
    "n_unsat = len(df_train[df_train['TARGET'] == 1]) \n",
    "sat_rate = 100*n_unsat/n_sat\n",
    "print (\"Total number of data: {}\".format(n_data))\n",
    "print (\"Number of features: {}\".format(n_features))\n",
    "print (\"Number of satisfied customer: {}\".format(n_sat))\n",
    "print (\"Number of dissatisfied customer: {}\".format(n_unsat))\n",
    "print (\"% of dissatisfied customer: {:.2f}%\".format(sat_rate))\n",
    "\n",
    "# testing set\n",
    "# label is not given to the testing set\n",
    "print (\"\\nTesting set:\")\n",
    "n_data  = len(df_test)\n",
    "# exclude first colum ID (substract 1)\n",
    "n_features = df_test.shape[1]-1 \n",
    "print (\"Total number of data: {}\".format(n_data))\n",
    "print (\"Number of features: {}\".format(n_features))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Remove Constant Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# removed constant columns: 34\n"
     ]
    }
   ],
   "source": [
    "# remove constant columns\n",
    "colsToRemove = []\n",
    "df0= df_train # orignial dataset\n",
    "for col in df_train.columns:\n",
    "    # column have 0 standard deviation\n",
    "    if df_train[col].std() == 0: \n",
    "        colsToRemove.append(col)\n",
    "# remove constant columns in the training set\n",
    "df_train.drop(colsToRemove, axis=1, inplace=True) \n",
    "# remove constant columns in the test set\n",
    "df_test.drop(colsToRemove, axis=1, inplace=True) \n",
    "print(\"# removed constant columns: {}\".format(len(colsToRemove)))  \n",
    "#print(\"Train set size: {}\".format(df_train.shape))\n",
    "#print(\"Test set size: {}\".format(df_test.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ind_var2_0', 'ind_var2', 'ind_var27_0', 'ind_var28_0', 'ind_var28', 'ind_var27', 'ind_var41', 'ind_var46_0', 'ind_var46', 'num_var27_0', 'num_var28_0', 'num_var28', 'num_var27', 'num_var41', 'num_var46_0', 'num_var46', 'saldo_var28', 'saldo_var27', 'saldo_var41', 'saldo_var46', 'imp_amort_var18_hace3', 'imp_amort_var34_hace3', 'imp_reemb_var13_hace3', 'imp_reemb_var33_hace3', 'imp_trasp_var17_out_hace3', 'imp_trasp_var33_out_hace3', 'num_var2_0_ult1', 'num_var2_ult1', 'num_reemb_var13_hace3', 'num_reemb_var33_hace3', 'num_trasp_var17_out_hace3', 'num_trasp_var33_out_hace3', 'saldo_var2_ult1', 'saldo_medio_var13_medio_hace3']\n"
     ]
    }
   ],
   "source": [
    "print(colsToRemove) #printconstant columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Remove Duplicate Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'delta_imp_reemb_var33_1y3': ['delta_num_reemb_var33_1y3'], 'ind_var18_0': ['ind_var18'], 'delta_imp_reemb_var13_1y3': ['delta_num_reemb_var13_1y3'], 'ind_var26_0': ['ind_var26'], 'ind_var25_0': ['ind_var25'], 'num_var6_0': ['num_var29_0'], 'num_var26_0': ['num_var26'], 'ind_var40': ['ind_var39'], 'ind_var37_0': ['ind_var37'], 'num_var18_0': ['num_var18'], 'delta_imp_trasp_var33_in_1y3': ['delta_num_trasp_var33_in_1y3'], 'saldo_var13_medio': ['saldo_medio_var13_medio_ult1'], 'num_var40': ['num_var39'], 'num_var34_0': ['num_var34'], 'num_var32_0': ['num_var32'], 'ind_var13_medio_0': ['ind_var13_medio'], 'num_var6': ['num_var29'], 'num_var13_medio_0': ['num_var13_medio'], 'ind_var32_0': ['ind_var32'], 'delta_imp_reemb_var17_1y3': ['delta_num_reemb_var17_1y3'], 'delta_imp_trasp_var17_in_1y3': ['delta_num_trasp_var17_in_1y3'], 'saldo_var6': ['saldo_var29'], 'ind_var34_0': ['ind_var34'], 'num_var37_0': ['num_var37'], 'num_var25_0': ['num_var25'], 'ind_var6_0': ['ind_var29_0'], 'delta_imp_trasp_var33_out_1y3': ['delta_num_trasp_var33_out_1y3'], 'ind_var6': ['ind_var29'], 'delta_imp_trasp_var17_out_1y3': ['delta_num_trasp_var17_out_1y3']}\n",
      "# duplicated columns with distinct values: 29\n",
      "# removed duplicated columns: 29\n"
     ]
    }
   ],
   "source": [
    "# remove duplicate columns\n",
    "colsToRemove = [] # columns to remove\n",
    "colsScaned = [] # columns scaned\n",
    "dupList = {} # a dictionary of dupicate columns with distinct values\n",
    "# the keys are the first column of the dupicated columns\n",
    "# the entries are columns which has the same value as the key \n",
    "columns = df_train.columns\n",
    "for i in range(len(columns)-1):\n",
    "    # search through every column\n",
    "    v = df_train[columns[i]].values\n",
    "    dupCols = [] # dumpicated columns\n",
    "    for j in range(i+1,len(columns)):\n",
    "    # compare if the two column are equal\n",
    "        if np.array_equal(v,df_train[columns[j]].values):  \n",
    "        # if yes add to the columns to be removed\n",
    "            colsToRemove.append(columns[j])\n",
    "            if columns[j] not in colsScaned:\n",
    "            # only add new entry if the column haven't be scaned before\n",
    "                dupCols.append(columns[j]) \n",
    "                colsScaned.append(columns[j]) # add the column as scaned\n",
    "                dupList[columns[i]] = dupCols # update the key in the dictionary\n",
    "\n",
    "# print the dupicated columns as a dictionary\n",
    "# the keys are distinct dupicated columns\n",
    "# the entries are columns which has the same value as the key \n",
    "print(dupList)\n",
    "print(\"# duplicated columns with distinct values: {}\".format(len(dupList)))  \n",
    "\n",
    "df_train.drop(colsToRemove, axis=1, inplace=True) \n",
    "# remove duplicate columns in the training set\n",
    "df_test.drop(colsToRemove, axis=1, inplace=True) \n",
    "# remove duplicate columns in the test set\n",
    "print(\"# removed duplicated columns: {}\".format(len(colsToRemove)))  \n",
    "#print(\"Train set size: {}\".format(df_train.shape))\n",
    "#print(\"Test set size: {}\".format(df_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ind_var29_0', 'ind_var29', 'ind_var13_medio', 'ind_var18', 'ind_var26', 'ind_var25', 'ind_var32', 'ind_var34', 'ind_var37', 'ind_var39', 'num_var29_0', 'num_var29', 'num_var13_medio', 'num_var18', 'num_var26', 'num_var25', 'num_var32', 'num_var34', 'num_var37', 'num_var39', 'saldo_var29', 'saldo_medio_var13_medio_ult1', 'delta_num_reemb_var13_1y3', 'delta_num_reemb_var17_1y3', 'delta_num_reemb_var33_1y3', 'delta_num_trasp_var17_in_1y3', 'delta_num_trasp_var17_out_1y3', 'delta_num_trasp_var33_in_1y3', 'delta_num_trasp_var33_out_1y3']\n"
     ]
    }
   ],
   "source": [
    "print(colsToRemove) # print Duplicate columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Make Train and Test Set\n",
    "Construct the training and test set from the original data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: (76020, 306)\n",
      "Test set size: (75818, 306)\n"
     ]
    }
   ],
   "source": [
    "# make train and test set\n",
    "id_test = df_test['ID'] # get the id of the test set data\n",
    "y_train = df_train['TARGET'] # get the label of the training set\n",
    "X_train = df_train.drop(['ID','TARGET'], axis=1) \n",
    "# remove the columns of ID and Label in training set\n",
    "X_test = df_test.drop(['ID'], axis=1) \n",
    "# remove the columns of ID  in test set\n",
    "\n",
    "print(\"Train set size: {}\".format(X_train.shape))\n",
    "print(\"Test set size: {}\".format(X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Exploratory Data Analysis\n",
    "The code below will visualzie by reducing it in 2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 PCA\n",
    "conduct Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: (76020L, 306L)\n"
     ]
    }
   ],
   "source": [
    "# rescale the data for PCA\n",
    "scaler = preprocessing.MinMaxScaler() # max min scaling\n",
    "X_train_normalized = scaler.fit_transform(X_train)\n",
    "print(\"Train set size: {}\".format(X_train_normalized.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xab916d8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7d89b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA\n",
    "# n_components equal # feature\n",
    "pca = PCA(n_components=X_train_normalized.shape[1]) \n",
    "pca.fit(X_train_normalized)\n",
    "np.set_printoptions(precision=2, suppress=True) # set printing format\n",
    "# plot variance explained ratio\n",
    "plt.plot(range(1,307),np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of principle components')\n",
    "plt.ylabel('variance explantion rate')\n",
    "plt.title(\" Variance explained ratio\")\n",
    "#print(np.cumsum(pca.explained_variance_ratio_))\n",
    "\n",
    "# Print the components and the amount of variance in the data\n",
    "# contained in each dimension\n",
    "# print (\"Principle components:\")\n",
    "# print (pca.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x162ef2e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XsNvja3h8APg3btwkE2Pe+ngLz89ZT0pyIvfcMIoeHewswRjTcPUNu12bMDCh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\nu+H12w/w3ifb6dIhla/YsBf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7d8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot variance explantion rate of first 10 PCs\n",
    "plt.plot(range(1,11),np.cumsum(pca.explained_variance_ratio_[:10]))\n",
    "plt.xlabel('number of principal components')\n",
    "plt.ylabel('variance explantion rate')\n",
    "plt.title(\" Variance explained ratio (first 10 PC)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Project on 2 PCs\n",
    "pca_2d = PCA(n_components = 2) # 2 principle components\n",
    "pca_2d.fit(X_train_normalized)\n",
    "# reduce the dataset to 2 pcs\n",
    "reduced_data = pca_2d.fit_transform(X_train_normalized) \n",
    "#np.set_printoptions(precision=0, suppress=True)\n",
    "#print (reduced_data[:10])  # print upto 10 elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Iscl4A8HT3n1pmApJqWnohoHD4cBE4VTKxxS7VUNYc342QRBOPxGQCcKfkCxD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P9w9u+aDls2WbKcPoT1ub6hIQGHY35AFUAipPolwZwRWRyLD9w9HhN8v9afXDCVT728naaoyewR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\nTJnAHy49n99MmUCKpuFQVaatSQRC01av5ZtlRdx8Tilpubk8tHQp2Tk5vP7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48ZLzqQ+G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/BoGks2bSVLd8ksSSGEECdEUgIyn8+XCvwcCCfj/EIcqd7ArjdAG254CGFys69UZkkKIYQ4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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162ef278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization PCA in 2D\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "colors = [(0.0, 0.63, 0.69), 'black']\n",
    "classes = [0,1]\n",
    "labels = [\"Satisfied customer\", \"Unsatisfied customer\"]\n",
    "markers = [\"o\", \"D\"]\n",
    "for class_ix, marker, color, label in zip(classes, markers, colors, labels):\n",
    "    ax.scatter(reduced_data[np.where(y_train == class_ix), 0],\n",
    "               reduced_data[np.where(y_train == class_ix), 1],\n",
    "               marker=marker, color=color, edgecolor='whitesmoke',\n",
    "               linewidth='1', alpha=0.9, label=label)\n",
    "    ax.legend(loc='best')\n",
    "plt.title(\" Principal Components Analysis\")\n",
    "plt.xlabel(\"Principal Axis 1\")\n",
    "plt.ylabel(\"Principal Axis 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model selection\n",
    "The code below will compare 7 different algorithms using the AUROC score and 3-fold cross-validation \n",
    "The classifiers are in their default setting and not fine-tuned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define scoring function (performance metrics)\n",
    "def score_model(clf):\n",
    "    print (\"\\nClassifier: {}...\".format(clf.__class__.__name__))\n",
    "    start = time.time()\n",
    "    # use 3-fold CV\n",
    "    scores = cross_validation.cross_val_score(clf, X_train, y_train,\n",
    "                                              scoring='roc_auc', cv=3) \n",
    "    end = time.time()\n",
    "    print (\"time (secs): {:.3f}\".format(end - start))\n",
    "    print (\"roc_auc: {:.3f}\".format(scores.mean()))\n",
    "    return scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classifier: DecisionTreeClassifier...\n",
      "time (secs): 7.438\n",
      "roc_auc: 0.572\n",
      "\n",
      "Classifier: GaussianNB...\n",
      "time (secs): 2.192\n",
      "roc_auc: 0.513\n",
      "\n",
      "Classifier: LogisticRegression...\n",
      "time (secs): 22.512\n",
      "roc_auc: 0.604\n",
      "\n",
      "Classifier: AdaBoostClassifier...\n",
      "time (secs): 21.312\n",
      "roc_auc: 0.826\n",
      "\n",
      "Classifier: RandomForestClassifier...\n",
      "time (secs): 4.377\n",
      "roc_auc: 0.676\n",
      "\n",
      "Classifier: BaggingClassifier...\n",
      "time (secs): 42.301\n",
      "roc_auc: 0.696\n",
      "\n",
      "Classifier: GradientBoostingClassifier...\n",
      "time (secs): 214.484\n",
      "roc_auc: 0.833\n"
     ]
    }
   ],
   "source": [
    "# Compare different algrithem\n",
    "scores = {}\n",
    "# Decision Tree\n",
    "scores['tree'] = score_model(tree.DecisionTreeClassifier()) \n",
    "# naive bayes\n",
    "scores['gaussian'] = score_model(naive_bayes.GaussianNB())\n",
    "# logistic regression\n",
    "scores['logistic_regression'] = score_model(linear_model.LogisticRegression()) \n",
    "\n",
    "# ensemble methors\n",
    "# AdaBoost\n",
    "scores['ada_boost'] = score_model(ensemble.AdaBoostClassifier()) \n",
    "# Random Forest\n",
    "scores['random_forest'] = score_model(ensemble.RandomForestClassifier()) \n",
    "# bagging\n",
    "scores['bagging'] = score_model(ensemble.BaggingClassifier()) \n",
    "# gradient boosting\n",
    "scores['gradient_boosting'] = score_model(ensemble.GradientBoostingClassifier()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4  Feature Selection\n",
    "The data importance is selected by the Gradient Boosting classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Feature importance\n",
    "rank the feature importance according the the classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
      "              max_depth=3, max_features=None, max_leaf_nodes=None,\n",
      "              min_samples_leaf=1, min_samples_split=2,\n",
      "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "              presort='auto', random_state=42, subsample=1.0, verbose=0,\n",
      "              warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# Use GradientBoosting to find feature importance\n",
    "clf = ensemble.GradientBoostingClassifier(random_state = 42) # define classifier\n",
    "clf.fit(X_train, y_train) # fit trainig data\n",
    "print(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n"
     ]
    }
   ],
   "source": [
    "# list feature importance\n",
    "importances = clf.feature_importances_ \n",
    "# get feature importance from the classifier\n",
    "indices = np.argsort(importances)[::-1]\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "## the list is very long, so comment it out\n",
    "#colsToSelect = []\n",
    "#for f in range(X_train.shape[1]):\n",
    "#    print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], \n",
    "#                                      X_train.columns[indices[f]], \n",
    "#                                      importances[indices[f]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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kER+cnjPgz1Ca4HnAPwIP9PKcSX087s05lNnzZetyl92AtW3vI2lVYM+WMRYC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\n2t6hzXl/LWk2cDml0d979OH7PeI+0fYRERExfhLoE31jECL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3P0n6z/p6BvD0Duc+FPhJ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\nIiIiIrrg/wMZmQmJ+yO0ewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d3f12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizate the most important 100 features\n",
    "feat_imp = pandas.Series(clf.feature_importances_, index=X_train.columns)\n",
    "feat_imp.sort_values(inplace=True,ascending =False )\n",
    "# plot the importance of the first 100 features\n",
    "ax =feat_imp.head(100).plot(kind='barh', figsize=(10,20), align='center',\n",
    "                           title='Feature Importance')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MrdbV0cj+rgGWJElSpRiAJUmSVCkGYEmSJFWKL4JTaVWzC5CkrWAVUGt2EZJajAFYAGTO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\nnAvF12iIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162cbf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizate the most important 10 features\n",
    "feat_imp = pandas.Series(clf.feature_importances_, index=X_train.columns)\n",
    "feat_imp.sort_values(inplace=True,ascending =False )\n",
    "#print(feat_imp.head(10))\n",
    "ax =feat_imp.head(10).plot(kind='barh', figsize=(10,5), align='center',\n",
    "                           title='Feature importance')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Feature Selection\n",
    "Reduce the number of # features to accelerate the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of feature selected: 92\n"
     ]
    }
   ],
   "source": [
    "# select the first 92 most important features.\n",
    "colsToSelect = []\n",
    "for f in range(0,92):\n",
    "    colsToSelect.append(X_train.columns[indices[f]])\n",
    "print (\"number of feature selected: {:.0f}\".format(len(colsToSelect)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: (76020, 92)\n",
      "Test set size: (75818, 92)\n"
     ]
    }
   ],
   "source": [
    "# select feature in new data set\n",
    "y_train2 = df_train['TARGET'] # data label\n",
    "X_train2 = df_train[colsToSelect] # new training set\n",
    "X_test2 = df_test[colsToSelect] # new test set\n",
    "print(\"Train set size: {}\".format(X_train2.shape))\n",
    "print(\"Test set size: {}\".format(X_test2.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Data Visualizaton\n",
    "The code below will visualize  first 5 most important features through:\n",
    "* Pair plot\n",
    "* Correlation plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# select the first 5 most important features to visualize\n",
    "cols2see = []\n",
    "for f in range(0,5):\n",
    "    cols2see.append(X_train.columns[indices[f]])\n",
    "cols2see.append('TARGET') # make another one with 'TARGET'\n",
    "data2see = df_train[cols2see]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['saldo_var30', 'var15', 'var38', 'saldo_medio_var5_ult3', 'saldo_medio_var5_hace3', 'TARGET']\n"
     ]
    }
   ],
   "source": [
    "# print the 5 variable to investigate\n",
    "print(cols2see)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xf6329e8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yzUgnnm+XtpmFC99l1qwZvPvuh6SkpLb7PngTiq//4aIjhG3p0vbiTUZGMmvW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Io7E49VVB7nn0xE377q2refLFVICQfROe342yLOwhSRIHjinLyUze+L/TE5bkzyJ8tG1gVgGp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113bf320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pair plot the 5 variables\n",
    "g =sns.pairplot(df_train[['saldo_var30', 'var15', 'var38',\n",
    "                          'saldo_medio_var5_ult3', 'saldo_medio_var5_hace3',\n",
    "                          'TARGET']],\n",
    "             hue=\"TARGET\", size=2, diag_kind=\"kde\")\n",
    "g.fig.suptitle('Pairplot of the 5 most important features',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dalaska\\Anaconda2\\lib\\site-packages\\seaborn\\linearmodels.py:1285: UserWarning: The `corrplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `corrplot` function has been deprecated in favor \"\n",
      "C:\\Users\\Dalaska\\Anaconda2\\lib\\site-packages\\seaborn\\linearmodels.py:1351: UserWarning: The `symmatplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `symmatplot` function has been deprecated in favor \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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APkKI3UKIvbrBfiyeaMNr/6nG77//lqCgoowePZGaNZ/mp58OMn36FMLCwvj8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\n4BVCCBf7LzA+synqqGZrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162c1198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation of the 5 most important features\n",
    "# select the first 5 most important features to visualize\n",
    "cols2see = []\n",
    "for f in range(0,5):\n",
    "    cols2see.append(X_train.columns[indices[f]])\n",
    "data2see = df_train[cols2see]\n",
    "ax = plt.axes()\n",
    "sns.corrplot(data2see,ax = ax)\n",
    "ax.set_title('Corelation plot of the 5 most important features',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Parameter Tuning\n",
    "The process of the parameter tuning is described as follows:\n",
    "* Choose a relatively high ‘learning rate’.\n",
    "* Determine the optimum ‘max_features’.\n",
    "* Determine the optimum ‘n_estimators’.\n",
    "* Tune tree-specific parameters including ‘min_samples_leaf’ ‘max_depth’.\n",
    "* Tune the ‘subsample’ parameters.\n",
    "* Fine tuning the ‘learning rate’."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1  Tuning max_features \n",
    "max_features determines the number of features to be considered while searching for the best split. The higher values can lead to over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning max_features\n",
      "max_features: 10.000\n",
      "Running time (secs): 67.376\n",
      "roc_auc: 0.83343\n",
      "max_features: 15.000\n",
      "Running time (secs): 70.632\n",
      "roc_auc: 0.83607\n",
      "max_features: 18.000\n",
      "Running time (secs): 72.716\n",
      "roc_auc: 0.83532\n",
      "max_features: 20.000\n",
      "Running time (secs): 73.194\n",
      "roc_auc: 0.83655\n",
      "max_features: 30.000\n",
      "Running time (secs): 77.630\n",
      "roc_auc: 0.83585\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x15978710>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CQzysmJMZ6HIkiMRMCWfRzDQWzUzDcRzqWo775ua1YI+0caThGEcajvHUK1VE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\nq55bd+nCOUw6bnzJaSRJkmpnsRvi+R2v8d1/7WL8uGYuv2B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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b1e59e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tuning max_features \n",
    "print (\"Tuning max_features\")\n",
    "# search for the optimal value\n",
    "max_features = [10,15,18,20,30]\n",
    "test_score = np.zeros(len(max_features))\n",
    "# Create different models \n",
    "for i, s in enumerate(max_features):\n",
    "    start = time.time()\n",
    "    print (\"max_features: {:.3f}\".format(s))\n",
    "    # Setup a classifer\n",
    "    clf = ensemble.GradientBoostingClassifier(max_features = s, \n",
    "                                              learning_rate=0.1,\n",
    "                                              random_state=42)                 \n",
    "    # use 4-fold CV\n",
    "    scores = cross_validation.cross_val_score(clf, X_train2, y_train2, scoring='roc_auc', cv=4)\n",
    "    test_score[i] = scores.mean() # report the mean      \n",
    "    end = time.time()\n",
    "    print (\"Running time (secs): {:.3f}\".format(end - start))    \n",
    "    print (\"roc_auc: {:.5f}\".format(scores.mean())) \n",
    "\n",
    "# Visual aesthetics\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(max_features, test_score, lw = 2, label = 'Testing score')\n",
    "plt.legend()\n",
    "plt.title('max_features', fontsize=18, y=1.03)\n",
    "plt.xlabel('max_features')\n",
    "plt.ylabel('auc score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1  Tuning n_estimators\n",
    "Parameter n_estimators determines the number of sequential trees to be modeled, A relatively large learning_rate is selected (0.1) to reduce the training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning n_estimators\n",
      "n_estimators: 50.000\n",
      "Running time (secs): 37.058\n",
      "roc_auc: 0.83229\n",
      "n_estimators: 100.000\n",
      "Running time (secs): 72.727\n",
      "roc_auc: 0.83532\n",
      "n_estimators: 130.000\n",
      "Running time (secs): 93.591\n",
      "roc_auc: 0.83577\n",
      "n_estimators: 150.000\n",
      "Running time (secs): 108.161\n",
      "roc_auc: 0.83584\n",
      "n_estimators: 170.000\n",
      "Running time (secs): 122.358\n",
      "roc_auc: 0.83612\n",
      "n_estimators: 200.000\n",
      "Running time (secs): 142.666\n",
      "roc_auc: 0.83583\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1530f1d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TPXCZqUnExYXufcvLy6C2tnlwT1BEBkQhT0QkBrS0d7F2SwUvv1XO0cbjp3y+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\nAktJwmERmBVj/NsQwueBLwJVJFfX3l+2HZIkSeohyhryJEm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d4af7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tunning n_estimators\n",
    "print (\"Tuning n_estimators\")\n",
    "# search for the optimal value\n",
    "n_estimators = [50,100,130,150,170,200]\n",
    "test_score = np.zeros(len(n_estimators))\n",
    "# Create different models\n",
    "for i, s in enumerate(n_estimators):\n",
    "    start = time.time()\n",
    "    print (\"n_estimators: {:.3f}\".format(s))\n",
    "    # setup a classifer\n",
    "    clf = ensemble.GradientBoostingClassifier(n_estimators = s, \n",
    "                                              learning_rate= 0.1,\n",
    "                                              max_features = 18,\n",
    "                                              random_state=42)                 \n",
    "    # use 4-fold CV\n",
    "    scores = cross_validation.cross_val_score(clf, X_train2, y_train2, scoring='roc_auc', cv=4)\n",
    "    test_score[i] = scores.mean()  # report the mean       \n",
    "    end = time.time()\n",
    "    print (\"Running time (secs): {:.3f}\".format(end - start))    \n",
    "    print (\"roc_auc: {:.5f}\".format(scores.mean())) \n",
    "\n",
    "# Visual aesthetics\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(n_estimators, test_score, lw = 2, label = 'Testing score')\n",
    "plt.legend()\n",
    "plt.title('n_estimators', fontsize=18, y=1.03)\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('auc score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Tree Parameters (max_depth, min_samples_leaf)\n",
    "The next step is to tune the tree-specific parameters. Two of the most important parameters are max_depth, min_samples_leaf, and min_samples_split\n",
    "* max_depth determines the maximum depth of a tree. The model with higher depth will tend to over-fitting because higher depth will allow the model to learn very specific to a particular sample.\n",
    "* min_samples_leaf defines the minimum samples required in a terminal node. Higher values prevent a model from over-fitting the data.\n",
    "* min_samples_split defines the minimum number of samples required in a node to be considered for splitting. Similar as min_samples_leaf, the higher values prevent a model from over-fitting the data. To reducing the searching space, we chose the number of min_samples_split to be 2 times the number of min_samples_leaf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning tree parameters: max_depth,min_samples_leaf\n",
      "max_depth: 2.000\n",
      "min_samples_leaf: 1.000\n",
      "Running time (secs): 100.709\n",
      "roc_auc: 0.83483\n",
      "max_depth: 2.000\n",
      "min_samples_leaf: 3.000\n",
      "Running time (secs): 100.433\n",
      "roc_auc: 0.83523\n",
      "max_depth: 2.000\n",
      "min_samples_leaf: 4.000\n",
      "Running time (secs): 101.046\n",
      "roc_auc: 0.83509\n",
      "max_depth: 2.000\n",
      "min_samples_leaf: 5.000\n",
      "Running time (secs): 100.867\n",
      "roc_auc: 0.83560\n",
      "max_depth: 2.000\n",
      "min_samples_leaf: 6.000\n",
      "Running time (secs): 100.597\n",
      "roc_auc: 0.83541\n",
      "max_depth: 3.000\n",
      "min_samples_leaf: 1.000\n",
      "Running time (secs): 106.982\n",
      "roc_auc: 0.83584\n",
      "max_depth: 3.000\n",
      "min_samples_leaf: 3.000\n",
      "Running time (secs): 106.833\n",
      "roc_auc: 0.83638\n",
      "max_depth: 3.000\n",
      "min_samples_leaf: 4.000\n",
      "Running time (secs): 106.799\n",
      "roc_auc: 0.83661\n",
      "max_depth: 3.000\n",
      "min_samples_leaf: 5.000\n",
      "Running time (secs): 106.718\n",
      "roc_auc: 0.83769\n",
      "max_depth: 3.000\n",
      "min_samples_leaf: 6.000\n",
      "Running time (secs): 107.142\n",
      "roc_auc: 0.83700\n",
      "max_depth: 4.000\n",
      "min_samples_leaf: 1.000\n",
      "Running time (secs): 115.356\n",
      "roc_auc: 0.83533\n",
      "max_depth: 4.000\n",
      "min_samples_leaf: 3.000\n",
      "Running time (secs): 115.117\n",
      "roc_auc: 0.83602\n",
      "max_depth: 4.000\n",
      "min_samples_leaf: 4.000\n",
      "Running time (secs): 115.050\n",
      "roc_auc: 0.83580\n",
      "max_depth: 4.000\n",
      "min_samples_leaf: 5.000\n",
      "Running time (secs): 113.621\n",
      "roc_auc: 0.83612\n",
      "max_depth: 4.000\n",
      "min_samples_leaf: 6.000\n",
      "Running time (secs): 111.334\n",
      "roc_auc: 0.83714\n",
      "max_depth: 5.000\n",
      "min_samples_leaf: 1.000\n",
      "Running time (secs): 122.809\n",
      "roc_auc: 0.83326\n",
      "max_depth: 5.000\n",
      "min_samples_leaf: 3.000\n",
      "Running time (secs): 122.156\n",
      "roc_auc: 0.83513\n",
      "max_depth: 5.000\n",
      "min_samples_leaf: 4.000\n",
      "Running time (secs): 121.835\n",
      "roc_auc: 0.83522\n",
      "max_depth: 5.000\n",
      "min_samples_leaf: 5.000\n",
      "Running time (secs): 120.736\n",
      "roc_auc: 0.83433\n",
      "max_depth: 5.000\n",
      "min_samples_leaf: 6.000\n",
      "Running time (secs): 120.572\n",
      "roc_auc: 0.83622\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x16fb3080>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XnTMyMpIJE17ANLeyY8d2Fix4hy5dujFq1GPY7bUXROv33ygREZF6zmN5WLzr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e3d7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tunning Tree Parameters (max_depth, min_samples_leaf)\n",
    "print (\"Tuning tree parameters: max_depth,min_samples_leaf\")\n",
    "# search for the optimal value\n",
    "min_samples_leaf= [1,3,4,5,6]\n",
    "max_depth = [2,3,4,5]\n",
    "# inicializing\n",
    "test_score = np.zeros([len(max_depth),len(min_samples_leaf)]) \n",
    "# Create different models\n",
    "for i, s in enumerate(max_depth):\n",
    "    for j, t in enumerate(min_samples_leaf):\n",
    "        start = time.time()\n",
    "        print (\"max_depth: {:.3f}\".format(s))\n",
    "        print (\"min_samples_leaf: {:.3f}\".format(t))\n",
    "        # Setup a classifer\n",
    "        clf = ensemble.GradientBoostingClassifier(n_estimators = 150,\n",
    "                                                  max_features = 18,\n",
    "                                                  learning_rate=0.1,\n",
    "                                                  min_samples_leaf = t, \n",
    "                                                  min_samples_split=2*t,\n",
    "                                                  max_depth = s,\n",
    "                                                  random_state=42)        \n",
    "        # use 4-fold CV\n",
    "        scores = cross_validation.cross_val_score(clf, X_train2, y_train2, \n",
    "                                                  scoring='roc_auc', cv=4)\n",
    "        test_score[i][j] = scores.mean() # report the mean          \n",
    "        end = time.time()\n",
    "        print (\"Running time (secs): {:.3f}\".format(end - start))    \n",
    "        print (\"roc_auc: {:.5f}\".format(scores.mean())) \n",
    "\n",
    "# Visual aesthetics\n",
    "plt.figure(figsize=(10,8))\n",
    "for i in range (len(max_depth)):\n",
    "    plt.plot(min_samples_leaf, test_score[i], lw = 2, \n",
    "             label = 'max_depth='+str(i+2))\n",
    "plt.legend()\n",
    "plt.title('tree parameters', fontsize=18, y=1.03)\n",
    "plt.xlabel('min_samples_leaf')\n",
    "plt.ylabel('auc score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Tuning  Subsample\n",
    "Subsample controls the fraction of observations to be selected for each tree. The selection is done by random sampling. The values slightly less than 1 make the model more robust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning subsample\n",
      "subsample: 0.700\n",
      "Runinng time (secs): 106.038\n",
      "roc_auc: 0.83563\n",
      "subsample: 0.800\n",
      "Runinng time (secs): 105.813\n",
      "roc_auc: 0.83719\n",
      "subsample: 0.900\n",
      "Runinng time (secs): 105.088\n",
      "roc_auc: 0.83749\n",
      "subsample: 0.950\n",
      "Runinng time (secs): 104.564\n",
      "roc_auc: 0.83668\n",
      "subsample: 0.980\n",
      "Runinng time (secs): 104.746\n",
      "roc_auc: 0.83744\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x13f2cc18>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pbFtTwoYlBaqEFNckJ8Zx/8oidh0ONFl+7C13uR1SxBlpuTI3enbNGE3JnohE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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17061b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tuning Subsample\n",
    "print (\"Tuning subsample\")\n",
    "# search for the optimal value\n",
    "subsample = [0.7,0.8,0.9,0.95,0.98]\n",
    "test_score = np.zeros(len(subsample))\n",
    "# Create different models \n",
    "for i, s in enumerate(subsample):\n",
    "    start = time.time()\n",
    "    print (\"subsample: {:.3f}\".format(s))\n",
    "    # Setup a classifer\n",
    "    clf = ensemble.GradientBoostingClassifier(subsample = s,\n",
    "                                              n_estimators = 150,\n",
    "                                              max_features = 18,\n",
    "                                              max_depth = 3,\n",
    "                                              min_samples_leaf = 5, \n",
    "                                              min_samples_split=10,\n",
    "                                              learning_rate=0.1,\n",
    "                                              random_state=42)                 \n",
    "\n",
    "    # use 4-fold CV\n",
    "    scores = cross_validation.cross_val_score(clf, X_train2, y_train2,\n",
    "                                              scoring='roc_auc', cv=4)\n",
    "    test_score[i] = scores.mean() # report the mean          \n",
    "    end = time.time()\n",
    "    print (\"Runinng time (secs): {:.3f}\".format(end - start))    \n",
    "    print (\"roc_auc: {:.5f}\".format(scores.mean())) \n",
    "\n",
    "# Visual aesthetics\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(subsample, test_score, lw = 2, label = 'Testing score')\n",
    "plt.legend()\n",
    "plt.title('subsample', fontsize=18, y=1.03)\n",
    "plt.xlabel('subsample')\n",
    "plt.ylabel('auc score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 5.4  Fine Tune Learning Rate\n",
    "Fine tune the learning rate. Decrease the learning rate and at the same time proportionally increase n_estimators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fine tuning learning rate\n",
      "learn_rate: 0.100\n",
      "Runinng time (secs): 104.696\n",
      "roc_auc: 0.83668\n",
      "learn_rate: 0.050\n",
      "Runinng time (secs): 209.972\n",
      "roc_auc: 0.83766\n",
      "learn_rate: 0.025\n",
      "Runinng time (secs): 317.067\n",
      "roc_auc: 0.83748\n",
      "learn_rate: 0.013\n",
      "Runinng time (secs): 422.893\n",
      "roc_auc: 0.83715\n",
      "learn_rate: 0.006\n",
      "Runinng time (secs): 533.468\n",
      "roc_auc: 0.83477\n",
      "learn_rate: 0.003\n",
      "Runinng time (secs): 645.348\n",
      "roc_auc: 0.82889\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2e41b710>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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71BIW9nQ1uzmSJOkgYaibZ95PJ0mSmsFQN8/Kiw4b6iRJUiMZ6ubRxq1DbNwy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\n/weMy6sfYLJkHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124e62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fine tune the learning rate\n",
    "print (\"Fine tuning learning rate\")\n",
    "# search for the optimal value\n",
    "learn_rate = [0.1,0.05,0.025,0.0125,0.00625,0.003125]\n",
    "test_score = np.zeros(len(learn_rate))\n",
    "# Create different models\n",
    "for i, s in enumerate(learn_rate):\n",
    "    start = time.time()\n",
    "    print (\"learn_rate: {:.3f}\".format(s))\n",
    "    # Setup a classifer\n",
    "    # increase n_estimators \n",
    "    clf = ensemble.GradientBoostingClassifier(learning_rate=s,\n",
    "                                              n_estimators = 150*(i+1),\n",
    "                                              max_features = 18, \n",
    "                                              max_depth= 3, \n",
    "                                              subsample = 0.95,\n",
    "                                              min_samples_leaf=5,\n",
    "                                              min_samples_split=10,\n",
    "                                             random_state = 42)                \n",
    "    # use 4-fold CV\n",
    "    scores = cross_validation.cross_val_score(clf, X_train2, y_train2, \n",
    "                                              scoring='roc_auc', cv=4)\n",
    "    test_score[i] = scores.mean() # report the mean        \n",
    "    end = time.time()\n",
    "    print (\"Runinng time (secs): {:.3f}\".format(end - start))    \n",
    "    print (\"roc_auc: {:.5f}\".format(scores.mean())) \n",
    "\n",
    "# Visual aesthetics\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(learn_rate, test_score, lw = 2, label = 'Testing set')\n",
    "plt.legend()\n",
    "plt.title('Learning rate', fontsize=18, y=1.03)\n",
    "plt.xlabel('Learning rate')\n",
    "plt.ylabel('auc score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Verficiation\n",
    "Study the learning curve (classifer performance using the trainig set and test set) of the classifier using different numbers of data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# choose performance metric\n",
    "def performance_metric(y_true, y_pred):\n",
    "    \"\"\" Calculates and returns the total score between true and predicted values\n",
    "        based on a performance metric chosen by the student. \"\"\"\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_true, y_pred[:,1])\n",
    "    score = metrics.auc(fpr, tpr)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# learning rate\n",
    "def learning_curves(X, y,clf):\n",
    "    \"\"\" plot the learning curve for the train and testing data set. \"\"\"\n",
    "    print (\"Creating learning curve\")\n",
    "    # Create the figure \n",
    "    plt.figure(figsize=(10,8))\n",
    "    # We will vary the training set size\n",
    "    sizes = [1000,5000,10000,20000,30000,40000,50000,60000,76020]\n",
    "    train_score = np.zeros(len(sizes)) # score of the training set\n",
    "    test_score = np.zeros(len(sizes)) # score of the test set\n",
    "    # Create different models\n",
    "    for i, s in enumerate(sizes):\n",
    "            print(s)    \n",
    "            # Find the performance on the training set\n",
    "            # use 4-fold CV\n",
    "            scores = cross_validation.cross_val_score(clf, X[:s], y[:s],\n",
    "                                                      scoring='roc_auc', cv=4)\n",
    "            test_score[i] = scores.mean()    \n",
    "            print (\"test roc_auc: {:.5f}\".format(test_score[i])) \n",
    "\n",
    "            # Find the performance on the testing set\n",
    "            # use all the training data\n",
    "            clf.fit(X[:s], y[:s])\n",
    "            y_pred = clf.predict_proba(X[:s])\n",
    "            train_score[i] = performance_metric(y[:s], y_pred)\n",
    "            print (\"train roc_auc: {:.5f}\".format(train_score[i]))\n",
    "\n",
    "    # Visual aesthetics\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.plot(sizes, train_score, lw = 2, label = 'Testing set')\n",
    "    plt.plot(sizes, test_score, lw = 2, label = 'Testing set')\n",
    "    plt.legend()\n",
    "    plt.title('Learning Curve', fontsize=18, y=1.03)\n",
    "    plt.xlabel('number of samples')\n",
    "    plt.ylabel('auc score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating learning curve\n",
      "1000\n",
      "test roc_auc: 0.78682\n",
      "train roc_auc: 0.99311\n",
      "5000\n",
      "test roc_auc: 0.82273\n",
      "train roc_auc: 0.93782\n",
      "10000\n",
      "test roc_auc: 0.83304\n",
      "train roc_auc: 0.91052\n",
      "20000\n",
      "test roc_auc: 0.82232\n",
      "train roc_auc: 0.87572\n",
      "30000\n",
      "test roc_auc: 0.82790\n",
      "train roc_auc: 0.86489\n",
      "40000\n",
      "test roc_auc: 0.83157\n",
      "train roc_auc: 0.86335\n",
      "50000\n",
      "test roc_auc: 0.83180\n",
      "train roc_auc: 0.86070\n",
      "60000\n",
      "test roc_auc: 0.83735\n",
      "train roc_auc: 0.86052\n",
      "76020\n",
      "test roc_auc: 0.83766\n",
      "train roc_auc: 0.85695\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124ecb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rSvq267WIiIjIQDAkAl1RoRvoVhZXMEiu6hURERFpN+gvigAYNyKT7IwUKqsb2bG3lrHB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\nlbD/2AFWblsNZKdbJUmSiqbS81otBd4BfCOlNIu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13a72c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = ensemble.GradientBoostingClassifier(learning_rate=0.05,\n",
    "                                          n_estimators = 300,\n",
    "                                          max_features = 18,\n",
    "                                          subsample = 0.95,\n",
    "                                          max_depth= 3,                                           \n",
    "                                          min_samples_leaf=5,\n",
    "                                          min_samples_split=10,\n",
    "                                         random_state = 42)\n",
    "learning_curves(X_train2, y_train2,clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Predict the Test Set\n",
    "predict the test set and make submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting done\n",
      "Prediction done\n"
     ]
    }
   ],
   "source": [
    "# final classifier\n",
    "clf = ensemble.GradientBoostingClassifier(learning_rate=0.05,\n",
    "                                          n_estimators = 150,\n",
    "                                          max_features = 18,\n",
    "                                          subsample = 0.95,\n",
    "                                          max_depth= 3,                                           \n",
    "                                          min_samples_leaf=5,\n",
    "                                          min_samples_split=10,\n",
    "                                          random_state = 42)\n",
    "clf.fit(X_train2, y_train2) # fit the data\n",
    "print(\"Fitting done\")\n",
    "y_pred = clf.predict_proba(X_test2)\n",
    "submission = pandas.DataFrame({\"ID\":id_test, \"TARGET\":y_pred[:,1]})\n",
    "submission.to_csv(\"submission_f.csv\", index=False)\n",
    "print(\"Prediction done\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
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